A Comprehensive Kinetics Model for Light Oil Oxidation/Combustion Reactions under High Pressure Air Injection Process (HPAI)
Bibliographic record
Abstract
Abstract It is acknowledged that chemical reactions and their kinetics play a major role on the success of both light and heavy oil air injection processes. Historically, Light oil reactions have been characterized mostly using conventional heavy oil kinetics models. However, sensitivity of the reaction kinetics to phase behavior and compositional changes in light oils call for a comprehensive study of kinetics of light oil oxidation. This paper provides a new and comprehensive kinetic model for light oils oxidation/combustion reactions under HPAI, through experimental studies and numerical simulation. For the purpose of this research, a high pressure ramped temperature oxidation reactor (HPRTO) was designed. 15 air injection and nitrogen injection experiments were conducted on the mixture of light oil, water, and core. Based on the data, observations, and understandings achieved during the course of the experimental study, a reaction kinetic model was set up. This primary kinetic model was then incorporated into a thermal numerical simulation model to replicate the behavior of the conducted air injection tests. After fine-tuning of some kinetic parameters against the experimental data, the final proposed model was verified by its successful application to two other different cases. The significant finding of this research, which is the main feature of the proposed kinetic model, was the recognition and characterization of the potential vapor phase combustion reactions during the HPAI process and incorporating them into a light oil kinetics model. The model integrates the hydrocarbon compositional changes and energy generation characteristics of the oxygen addition or so called LTO reactions. Introducing the concept of flammability range into the kinetic model and defining the flammable limits for vapor fuel mixture in this model enables accurate prediction of ignition and exhaustion of the combustion reactions in the vapor phase. Lack of a reliable kinetics model for incorporation into field numerical simulations has been a limiting factor to the prospective vast applications of HPAI as an enhanced recovery method. The kinetics model proposed in this paper, which is supported by extensive experimental data, could successfully predict the oxidation/combustion behavior of two different light oils under the conditions associated with high-pressure air injection tests. The paper also presents a framework for application of the kinetics model to any light oil under HPAI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".